We modify Mixup[1]} or between-class (BC) learning[2]} to make slight randomness in background sound. These data augmentation techniques interpolate both features and labels between two data samples to create new data.
Using normalized log-mel spectrogram audio as input, our Mixup block randomly picks up a sample from a queue of past inputs and mixes it with the current input audio sample in a small ratio. As a result, mixed random audio becomes a part of the background sound in the current input.
